Evidence-to-decision: From exposome data to evidence to action through agentic AI

Public health decisions are uniquely difficult, weighing population benefits against harms, equity, resource constraints, and feasibility, often under deep uncertainty. The GRADE Evidence-to- Decision (EtD) framework, rooted in evidence-based medicine, offers a transparent route from evidence to action through twelve explicit criteria, and has recently been adapted for environmental and occupational health. A Human Exposome Project would generate evidence of a volume and complexity that breaks the manual assumptions on which EtD was built. I argue that agentic artificial intelligence, autonomous agents orchestrating multi-step scientific workflows, can operationalize each EtD criterion and make exposome-scale decision-making feasible, but only if it inherits the rigor of the evidence-based disciplines it is asked to accelerate. Six families of agents (evidence extraction, risk-of-bias assessment, uncertainty quantification, causality reasoning, cost-outcome analysis, and post-deployment validation) map cleanly onto the EtD criteria. Five governance requirements (traceability, versioning, context-of-use benchmarking, honest uncertainty, and human accountability) separate an evidence engine from a confident hallucination machine. The exposome demands nothing less.